Leveraging Sentinel-1 time-series data for mapping agricultural land cover and land use in the tropics

Synthetic aperture radar (SAR) can penetrate clouds, rendering these data particularly useful for mapping land cover and land use in tropical areas. In this study, we leverage the image processing and analysis platform built at Descartes Labs to analyze a time-series of Sentinel-1 SAR data acquired during the 2014 - 2015 growing season across the Vietnamese Mekong River Delta, a region that is dominated by rice paddy agriculture. Rice is a staple food for the majority of the global population, but production is threatened by expanding urban areas, rising temperatures, and encroaching sea levels. Most of the world's rice is grown in the monsoonal tropics, and frequent cloud cover makes monitoring the landscape challenging. Here, we illustrate how the unique phenology of rice is captured with SAR data to accurately map annual rice paddy extent, and we show how the method can be extended to also determine the amount of rice grown during each growing period within a season.